clarifying math

lerobot/common/datasets/abstract.py

@@ -187,7 +187,8 @@ class AbstractDataset(TensorDictReplayBuffer):
                 # Hint: to update the mean we need x̄ₙ = (Nₙ₋₁x̄ₙ₋₁ + Bₙxₙ) / Nₙ, where the subscript represents
                 # the update step, N is the running item count, B is this batch size, x̄ is the running mean,
                 # and x is the current batch mean. Some rearrangement is then required to avoid risking
-                # numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ.
+                # numerical overflow. Another hint: Nₙ₋₁ = Nₙ - Bₙ. Rearrangement yields
+                # x̄ₙ = x̄ₙ₋₁ + Bₙ * (xₙ - x̄ₙ₋₁) / Nₙ
                 mean[key] = mean[key] + this_batch_size * (batch_mean - mean[key]) / running_item_count
                 max[key] = torch.maximum(max[key], einops.reduce(batch[key], pattern, "max"))
                 min[key] = torch.minimum(min[key], einops.reduce(batch[key], pattern, "min"))